Semi-Supervised Text Classification
11 papers with code • 2 benchmarks • 1 datasets
In natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling.
In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches.
Ranked #3 on Text Classification on AG News
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.